11.2 Spectral density estimation

Spectral density estimation is a powerful tool for analyzing time series data. It breaks down complex signals into their frequency components, revealing hidden patterns and periodicities that might not be apparent in the raw data.

Choosing between parametric and non-parametric methods involves trade-offs in accuracy and flexibility. Smoothing techniques help balance the resolution-variance trade-off, allowing analysts to tailor their approach to the specific characteristics of their data.

Spectral Density Estimation

Concept of spectral density

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• Spectral density, also known as power spectral density (PSD), describes the distribution of power or variance across different frequencies in a time series
  • Provides information about the relative importance of different frequency components (low frequency, high frequency) in the series
• Represents the decomposition of the time series into a sum of sinusoidal components with different frequencies and amplitudes
  • Fourier transform of the autocovariance function of a stationary time series
• Analyzing spectral density allows for identifying dominant frequencies or periodicities (seasonal patterns, business cycles), detecting hidden patterns or cycles, and understanding the relative contributions of different frequency components to the overall variability of the series

Parametric vs non-parametric estimation methods

• Parametric methods assume a specific model for the time series (autoregressive (AR), moving average (MA)) and estimate the parameters of the assumed model to calculate the spectral density
  • Examples: Yule-Walker method, Burg method
  • Advantages: provide smooth spectral density estimates, require fewer data points
  • Disadvantages: rely on correct model specification, may not capture complex or non-linear relationships
• Non-parametric methods do not assume a specific model and estimate the spectral density directly from the data using techniques like periodogram or smoothed periodogram
  • Examples: Bartlett method, Welch method
  • Advantages: do not require assumptions about the underlying model, can capture complex or non-linear relationships
  • Disadvantages: may produce noisy or less smooth estimates, require more data points

Smoothing techniques for spectral density

• Smoothing techniques reduce variance and improve stability of spectral density estimates
• Windowing multiplies the time series by a window function (Hamming, Hann, Bartlett) before computing the periodogram
  • Reduces spectral leakage and improves resolution of the estimate
• Averaging divides the time series into overlapping or non-overlapping segments, computes the periodogram for each segment, and averages the periodograms
  • Reduces variance at the cost of reduced frequency resolution
  • Examples: Bartlett's method (non-overlapping segments), Welch's method (overlapping segments)
• Choice of window function and segment length depends on characteristics of the time series and desired trade-off between resolution and variance

Resolution vs variance trade-off

• Resolution: ability to distinguish between closely spaced frequency components in the spectral density estimate
  • Higher resolution allows for better separation of nearby frequencies
• Variance: variability or noise in the spectral density estimate
  • Lower variance leads to more stable and reliable estimates
• Inherent trade-off between resolution and variance in spectral density estimation
  • Increasing resolution (longer window or more segments) typically increases variance
  • Decreasing variance (shorter window or fewer segments) typically reduces resolution
• Choice of window length, segment length, and overlapping in smoothing techniques affects this trade-off
  • Longer windows or segments improve resolution but increase variance
  • Shorter windows or segments reduce variance but decrease resolution
• Optimal balance between resolution and variance depends on the specific application and characteristics of the time series being analyzed